Chemometric Investigation of Barley and Malt Data
Why this work is in the frame
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Bibliographic record
Abstract
Several hundred samples of barleys and corresponding pilot scale malts were analyzed for eight barley parameters and 15 malt parameters. Principal components analysis (PCA) was applied to the barley and malt data sets. The barley data had three significant PCs, corresponding to kernel size, germination rate and protein content, and moisture. The malt data had 5 significant components, largely corresponding to modification, extract, enzyme activity, nitrogenous substances, and wort pH. Pattern recognition of the barley and malt data sets was carried out with Linear Discriminant Analysis (LDA), k-Nearest Neighbor analysis (k-NN) and SIMCA. Classification of the barley samples into 2- or 6-row, winter or spring, origin country and cultivar was fairly successful. Classification of the malt samples into hulled or hull-less barleys, country of origin, and cultivar was quite successful; classification by crop year and 2- or 6-row barley was less successful. Models of malt parameters as a function of multiple barley measurements were constructed using partial least squares regression (PLSR). An excellent model of malt total protein (R2 = 0.74) was obtained. Fair models of friability, fine and coarse extract, soluble protein, Kolbach index, diastatic power and α-amylase activity were produced. Only poor models of the other parameters were obtained.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it